Other works have focused on proposing methods that generalize across different hardware settings. For example, policies conditioned on representations of agent body morphologies [1]}, [2]}, [3]} have been demonstrated to handle varying body types. Another way to handle embodiment differences involve training policies which can quickly adapt to changes in hardware or environment feedback using domain randomization [4]}, [5]} or meta-learning [6]}.
Other methods optimize not only control but also the robot hardware itself [7]}, [8]}, allowing for even more flexibility and potential competence in a given task.
